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---
pretty_name: Repository learning training dataset
tags:
- code-review
- github-data
- contrastive-learning
- fine-tuning
- semantic-indexing
- multi-modal
- jsonl
- faiss-index
- tree-sitter
license: mit
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- text-generation
- text-classification
- text-retrieval
- feature-extraction
source_datasets:
- github-repositories
annotations_creators:
- machine-generated
- expert-reviewed
---
# Repository Learning Training Dataset
This dataset contains training data extracted from GitHub repositories for training context-aware code review models. The dataset supports three primary machine learning tasks: contrastive learning, fine-tuning, and semantic indexing.
## Dataset Overview
**Purpose**: Enable training of AI models that understand repository-specific code review patterns and provide contextual feedback.
**Source**: GitHub repositories with rich pull request history and review comments.
## Dataset Structure
```
{repository-name}/
├── contrastive/
│ ├── changed_files_001.json # Files changed together (positive pairs)
│ ├── changed_files_002.json
│ └── ...
├── fine_tune/
│ ├── pr_reviews_001.jsonl # Instruction-following format
│ ├── pr_reviews_002.jsonl
│ └── ...
├── index/
│ ├── functions.json # AST-extracted function metadata
└── manifest.json # Processing metadata
```
## Data Components
### 1. Contrastive Learning Data (`/contrastive/`)
**Format**: JSON files containing file groupings for contrastive learning.
**Purpose**: Learn semantic relationships between code files based on change patterns.
**Structure**:
```json
{
"pr_12345": [
"src/components/Button.tsx",
"src/styles/button.css",
"tests/Button.test.tsx"
],
"pr_12346": [
"src/api/user.py",
"src/models/user.py",
"tests/test_user.py"
]
}
```
**Usage**: Files changed together form positive pairs; files from different PRs form negative pairs for contrastive learning.
### 2. Fine-Tuning Data (`/fine_tune/`)
**Format**: JSONL files with instruction-following examples.
**Purpose**: Adapt language models to repository-specific review patterns and conventions.
**Structure**:
```json
{
"prompt": "Code diff:\n```diff\n+def calculate_score(user_data):\n+ return sum(user_data.values())\n```\nPrevious comments:\n- alice: Consider input validation\n\nPlease write a code review comment:",
"completion": "Good addition! I'd suggest adding type hints and handling edge cases where user_data might be empty or contain non-numeric values."
}
```
**Features**:
- Chronological conversation context
- Multi-turn review discussions
- Repository-specific terminology and patterns
- Code diff context with surrounding discussion
### 3. Semantic Index Data (`/index/`)
**Format**: JSON metadata with function definitions and embeddings.
**Purpose**: Enable fast semantic search across repository functions and documentation.
**Structure** (`functions.json`):
```json
[
{
"file": "src/utils/parser.py",
"name": "parse_diff_hunk",
"start_line": 45,
"end_line": 67,
"code": "def parse_diff_hunk(hunk_text: str) -> DiffHunk:\n # Function implementation...",
}
]
```
**Components**:
- **AST Extraction**: Tree-sitter parsers for different programming languages
## Data Generation Pipeline
### Data Statistics
| Repository | PRs | Review Comments | Functions | Languages |
|------------|-----|-----------------|-----------|-----------|
| dotnet/xharness | 100 | 50 | 1500 | C# |
| dotnet/runtime | N/A | N/A | N/A | C#, c, c++ |
## Usage Examples
If you use this dataset, please refer to https://github.com/kotlarmilos/repository-learning |